CN116453320A - Reservoir area landslide deformation monitoring and early warning method based on sliding surface strain evolution - Google Patents
Reservoir area landslide deformation monitoring and early warning method based on sliding surface strain evolution Download PDFInfo
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Abstract
The invention discloses a reservoir area landslide deformation monitoring and early warning method based on landslide surface strain evolution, which comprises the steps of installing a high-spatial-resolution strain sensing optical cable in a landslide partition through vertical drilling, remotely acquiring underground strain distribution of a full-drilling hole of the landslide in real time, identifying the position of the landslide surface, and taking the landslide surface strain as a parameter representing global deformation of the landslide; consider the solar rainfall R i Intensity of daily rainfall I i Height L of reservoir water level i And daily water level fluctuation f i 4 influence factors, and establishing a prediction early warning model based on landslide strain evolution. The 4 effects are as followsFactors are used as input variables, sliding surface time sequence strain is clustered and decomposed into 3 characteristic deformation clusters Clusterj which are near steady state, acceleration deformation and steady state according to the evolution characteristics of the sliding surface time sequence strain, and the factors are used as output variables; and finding out a prediction rule corresponding to the characteristic deformation cluster of which the target output variable is the acceleration deformation, namely a hydro-meteorological early warning threshold value of the acceleration deformation of the landslide. The invention solves the problems of unknown identification of the multi-stage sliding surface of the landslide in the reservoir area, insufficient durability of monitoring deep large deformation and high early warning false alarm rate based on the surface deformation.
Description
Technical Field
The invention relates to landslide monitoring and early warning in the field of engineering geology, in particular to a reservoir landslide deformation monitoring and early warning method based on landslide strain evolution.
Background
The reconstruction of reservoir operation to the reservoir geological environment affects a large number of ancient landslides. Since the direction of movement of such landslide is generally nearly perpendicular to the main channel or tributary of the river, once a large landslide collapse disaster occurs, disaster chain effects of surge, landslide dam and downstream flood are often induced. Therefore, the safety monitoring and risk early warning of landslide in a reservoir area have attracted a great deal of attention.
In existing landslide deformation monitoring technologies, non-contact scanning (e.g., airborne InSAR, foundation InSAR) allows time-series surface deformation on a regional scale or a hillside scale to be obtained, but the deformation result is often used for trend analysis and is difficult in deformation quantification. GNSS displacement monitoring stations provide high-precision monitoring point ground surface deformation information, but the discrete monitoring points are challenging to large-area layout of landslide in an oversized reservoir area. In addition, the surface deformation is obviously influenced by human forces (such as agricultural production activities, infrastructure construction and transformation) and micro-landforms, the spatial heterogeneity characteristics are obvious, and high-probability dangerous deformation artifacts or even over-conservative early warning exists. It is therefore to be appreciated that the motion evolution process of landslide and efficient and reliable early warning need to rely on borehole monitoring.
In borehole-based deformation monitoring techniques, conventional sliding inclinometry provides accurate deep deformation, but fails within three-five years due to excessive deformation that is often encountered during flood season. Fixed inclinometry has obvious advantages in terms of durability and automation, but is limited by technology and cost, a group of 3 fixed inclinometry sensor arrays connected in series are usually arranged in one drilling hole, so that the technology can only obtain very limited deep relative deformation, and deformation information of a key engineering geological interface (such as a sliding surface) cannot be quantitatively described. Distributed optical fibers (DFOS) and quasi-distributed Fiber Bragg Gratings (FBGs) show great potential in geological engineering health monitoring, reflecting deep deformations by monitoring strain. The former provides higher spatial resolution (sub-meter level) but lower level of automated monitoring and early warning, and the latter enables remote real-time monitoring but is limited by lower spatial resolution (e.g. FBG sensors in one borehole typically not more than 10) and cannot obtain accurate strain distribution. Therefore, for large landslide with multistage slide development and evolution, the slide thickness is tens of centimeters-meters, and the sensor spatial resolution is a key for slide identification.
On the other hand, in the aspect of landslide early warning in a reservoir area, an early warning threshold value is set on the basis of earth surface displacement (or displacement rate) in the prior art. As previously mentioned, a large single point earth displacement observation does not mean a large deformation of the global landslide. The spatial dissimilarity characteristic of the earth surface displacement is obvious by forcing, so that the earth surface displacement-based early warning method is still challenging in timeliness and effectiveness, and the problem of 'over-warning' (namely conservative early warning) often exists (such as frequent false alarm). When the landslide is in the initial deformation stage, the instability and damage can not be caused even if the displacement rate is large. The other type is geological disaster risk early warning based on meteorological layers. As most landslide and collapse occur in rainy season, an empirical rainfall threshold is formed from the statistical perspective and monitoring data. Specifically, when the 24-hour rainfall exceeds 49.9mm or the 12-hour rainfall exceeds 29.9mm (heavy rain), a geological disaster heavy rain early warning is sent. The early warning method is wide in applicability, low in accuracy, only suitable for regional generalized early warning release and not suitable for single-seat landslide. In addition, the early warning method only gives a rainfall threshold value, and does not consider the influence factors such as the reservoir water level, the dynamics and the like, so that the current situation of high false alarm rate and low early warning reliability is caused.
From the above, in the prior art, a method capable of representing the real motion behavior of the global landslide is lacking, and the conventional remote real-time landslide monitoring cannot comprehensively reveal the underground evolution process of the landslide, and the early warning timeliness and effectiveness are insufficient based on the ground displacement prediction.
Disclosure of Invention
The invention aims to: in order to solve the problems of insufficient durability, low remote real-time automation level and high early warning false alarm rate of accurate monitoring of deep large deformation of a landslide in a reservoir area in the prior art, the invention provides a reservoir area landslide deformation monitoring and early warning method based on the strain evolution of a landslide surface, wherein the real deformation of the whole landslide is represented by the time sequence strain of underground full depth (namely depth to bedrock), so that a potential landslide surface is identified, further a hydrological early warning threshold value is established based on the strain evolution of the landslide surface by taking multiple influence factors into consideration, and the long-term monitoring and effective early warning level of the landslide in the reservoir area are improved; according to the high-spatial-resolution strain sensing optical cable, the sliding surface dynamics is accurately positioned and captured, a more reliable hydro-meteorological early warning threshold value can be obtained based on an early warning model of sliding surface strain evolution, and the problems that the multi-stage sliding surface of a sliding slope in a reservoir area is unidentified, the deep large deformation is accurately monitored, the durability is insufficient, and the early warning false alarm rate based on the surface deformation is high are solved.
The technical scheme is as follows: the invention discloses a reservoir landslide deformation monitoring and early warning method based on sliding surface strain evolution, which comprises the following steps:
(1) Monitoring station site selection and drilling: and selecting active landslide subareas and flat sites to vertically drill holes below the rock stratum, and establishing monitoring stations. The monitoring station comprises an in-situ monitoring unit, a data acquisition unit, namely an ultra-weak reflection grating demodulator, a data transmission unit and a user access unit. The data transmission unit is a wireless transmission module and is integrated in the earth surface monitoring station protection box. The user access unit is a computer.
(2) Ultra-weak reflection grating strain sensing optical cable installation: and (3) after the drilling in the step (1) is completed, an inverted distribution mode is adopted to install the ultra-weak reflection grating strain sensing optical cable. The ultra-weak reflection grating strain sensing optical cable is an ultra-weak reflection grating strain sensing optical cable, one end of the ultra-weak reflection grating strain sensing optical cable is connected to a data acquisition port of an ultra-weak reflection grating demodulator near an orifice, the middle position of the optical cable is connected with a gravity guide hammer and is fixed at the bottom of a hole to pre-pull the optical fiber, the redundancy of the optical cable at the bottom of the hole is 0.5m, the vertical optical cable is kept in a pre-pulling state all the time, and the other end of the optical cable is led out of the orifice. The optical cable connected with the data acquisition port of the ultra-weak reflection grating demodulator is called an A section by taking the hole bottom as a limit, and the other section of the hole bottom, which extends out of the hole after being redundant for one section, is called a B section.
(3) And (3) backfilling drilling: and (3) after the gravity guide hammer connected with the middle part of the ultra-weak reflection grating strain sensing optical cable in the step (2) is lowered to the hole bottom position, fixing the two ends of the ultra-weak reflection grating strain sensing optical cable extending out of the hole opening to enable the ultra-weak reflection grating strain sensing optical cable to be in a tensioning state all the time, backfilling the drilled holes, and filling the holes in sections according to the actual stratum according to the type of the rock-soil body in principle so as to approximate to the distribution of the actual stratum. Filling a complete stratum section with the depth of 3m from the bottom of the hole with a cement accelerator to play a role in relatively hard stratum; the upper broken rock interval is filled with fine sand; the uppermost crushed stone soil section is filled with fine sand and powdery clay in a ratio of 1:1.
(4) Remote real-time monitoring: and (3) backfilling to the hole and compacting, introducing the end A of the ultra-weak reflection grating strain sensing optical cable extending out of the hole into a protection box on the ground surface near the hole, connecting an optical fiber jumper wire to an acquisition port of the ultra-weak reflection grating demodulator, and debugging the ultra-weak reflection grating demodulator, a data transmission unit and a user access unit. And (3) after the backfilling of the drilling installation and the equipment debugging are finished, standing for one month, and taking the backfill as a monitoring initial period after the backfill is solidified and stabilized. And starting long-term remote real-time monitoring activities, and establishing time sequence strain data sets of the rock-soil bodies with different depths in the underground.
(5) Slip plane identification and time sequence strain cluster analysis: drawing a full-borehole strain profile evolution graph according to the time sequence strain dataset which is obtained by the user access unit and recorded in the step (4) in real time, and identifying a remarkable strain peak area as a potential sliding surface according to strain distribution along the full borehole, wherein the sliding surface has the characteristic of rapid increase of strain when the slope is deformed in an accelerating way; and (3) extracting a sliding surface strain time sequence, taking an average value of a plurality of strain data every day as a strain daily value, and further, obtaining a strain rate data set by difference. According to the evolution characteristics of the sliding surface strain rate along with time, the strain is qualitatively clustered into near-steady state, acceleration (strain mutation) deformation and steady state 3 types, and at the moment, the numerical variable is converted into a type variable (Clusterj, j=1, 2, 3), namely the type variable is the output variable of the early warning model. The method adopted by the clustering analysis follows the principle of 'data backtracking' to eliminate noise errors of a strain rate time sequence, namely, discontinuous strain change (singular value) is specified as possible noise data, zero-domain strain is designated as a steady state, 300 mu epsilon is a critical value of near steady state and acceleration deformation, the acceleration deformation section is clustered when the zero-domain strain exceeds the critical value, and the near steady state section is classified when the zero-domain strain is lower than the critical value.
(6) Establishing a predictive early warning model and a threshold criterion: taking the clustering result Clusterj of the slip surface strain rate obtained in the step (5) as an output variable, and taking rainfall R as an output variable i Intensity of rainfall I i Height L of reservoir water level i And water level fluctuation f i And (3) taking the daily value data set of the model as an input variable to establish a predictive early warning model. The predictive early warning model deduces the value of the output variable according to the value of the input variable, establishes threshold criteria of landslide underground strain evolution and external driving factors such as rainfall, rainfall intensity, reservoir water level elevation and reservoir water level fluctuation, and early warns the landslide deformation in the reservoir area. Specifically, the sample data (i.e., the input variables and the output variables described above) are divided into training and test sets at a ratio of 7:3. And repeatedly training the decision tree model through the input of the training set, and performing classification prediction on the new data object (test set) when the accuracy of the generated decision tree model reaches 60% -70% through the test set. The generated predictive early warning model predicts the value Clusterj of the output variable corresponding to the input variable according to the value of the input variable, and is presented as a series of 'if-then' sentences in a form so as to be understood and applied. The term "if-then" is a predictive rule that relates to deformation class, multiple impact factors, and specific hydrological thresholds. In summary, according to the correlation between the evolving slip surface strain and external driving factors (such as rainfall, rainfall intensity, reservoir water level elevation and reservoir water level fluctuation), threshold criteria of the slip surface acceleration deformation and different hydrological weather conditions are established so as to realize early warning, such as given reservoir water level elevation L i What rainfall intensity I i Under the condition, acceleration deformation clusteri (j=2) occurs.
In the step (6), sample data in the prediction early-warning model is divided into a training set and a testing set, the iteration times are set, the prediction early-warning model is repeatedly trained through the input of the training set, the testing set is used for classifying and predicting when the precision of the generated decision tree model reaches a set range, and the value of an output variable is deduced according to the value of an input variable by the generated decision tree model.
In the step (3), the complete stratum section with the depth of 3m from the bottom of the hole is filled with a cement accelerator to be used as a hard stratum; filling the broken rock interval with fine sand; the crushed stone soil section is filled with fine sand and powdery clay.
In the step (5), the sliding surface strain rates are qualitatively clustered according to the evolution characteristics of the sliding surface strain rates along with time, wherein the evolution characteristics of the strain rates and the time curve comprise small fluctuation of the strain rates, abrupt change of the strain rates and unchanged strain rates.
The in situ monitoring unit includes one or more vertical bores to which strain sensing fiber optic cables are mounted.
In the step (5), during time-series strain cluster analysis, a data backtracking principle is followed to eliminate noise errors of a strain rate time sequence.
In the step (2), the sampling interval d of the ultra-weak reflection grating strain sensing optical cable is less than or equal to 1.0m.
The ultra-weak reflection grating strain sensing optical cable in the step (2) comprises an ultra-weak reflection grating and an optical fiber transmission section.
In the step (2), the middle position of the ultra-weak reflection grating strain sensing optical cable, which is connected with the gravity guide hammer, is arranged at the bottom of the hole.
In the step (2), the A, B end is redundant by at least 2m near the orifice to assist the accurate positioning of the underground depth, in particular to facilitate the identification of the slip surface of the thin layer (which is tens of centimeters thick); the end B is also connected with an acquisition jumper wire for standby.
In step (2), the ultra-weak reflection grating strain sensing optical cable is installed and pre-tensioned in an inverted-type in the borehole, and the grating spacing in the A, B section of the vertical optical cable along the inner wall of the borehole is at most 0.5m.
Working principle: the predictive early warning model adopted in the step (6) adopts an improved decision tree model, features in the data are searched by learning the historical data with the existing definite results, and further the deduced simple decision rule is adopted, and then the newly generated data are classified and predicted based on the simple decision rule. And starting from the root node, testing the characteristic attribute of the variable in the item to be classified, selecting and outputting the characteristic attribute to the corresponding branch according to the characteristic of the value of the characteristic attribute until the leaf node, and taking the category of the leaf node as a decision result. And constructing a rule by each path from the root node to the leaf node of the decision tree, wherein the characteristics of the internal nodes on the path correspond to the rule conditions, namely the prediction rule/early warning conditions. The class labels of the leaf nodes correspond to the conclusion of the rule, namely the strain rate clustering result clusteri.
The prediction early warning model adopts a branch growth algorithm, a variable value segmentation algorithm and a pruning algorithm. The branch growth algorithm is used for determining the current optimal grouping variable, and takes the maximum information gain rate as a standard:
where GR (S, T) is the information gain ratio of the characteristic attribute T in the dataset S, IV (T) is the entropy of the attribute T, and IG (S, T) is the information gain of the attribute T. When the value of the attribute T is large, the purity of IV (T) is low, that is, the value of IV (T) is large, so that the value of GR (S, T) becomes small. This corresponds to a penalty mechanism being assigned when a property has more values. At the same time, attributes that are not properly classified are assigned a higher weight so that subsequently trained models prioritize them. The growth process of the predictive early warning model is a process of continuously grouping input variables according to output variables. Each branch of the predictive early-warning model grows gradually in the process of continuously grouping the input data sets, and when the continuous grouping of the data sets is not meaningful any more, the growth process of the predictive early-warning model ends.
The sample partition is divided into a training set and a test set according to the proportion of 7:3 or 8:2, the sample is repeatedly trained for improving the model prediction robustness by setting the iteration number as N, and finally, the new data object (test data set) is predicted in a classified mode when the prediction early-warning model generated by the test sample pair is about 60-70%.
The generated predictive early warning model deduces the value of an output variable, namely a strain rate clustering result, (such as Cluster 1) according to the value of a new data input variable. In summary, a threshold criterion of landslide underground strain evolution and external driving factors (rainfall and reservoir water level) is established according to the generated predictive early warning model.
In general, the reservoir area landslide deformation monitoring and early warning method based on the landslide strain evolution comprises the steps of installing an ultra-weak reflection grating strain sensing optical cable with high spatial resolution through vertical drilling holes in an active landslide partition, and remotely acquiring the underground strain of a full-drilling hole of the landslide in real time to characterize the landslide deformation; identifying the position of a sliding surface and taking the sliding surface strain as a parameter for representing global deformation of the sliding surface; consider the solar rainfall R i Intensity of daily rainfall I i Height L of reservoir water level i And daily water level fluctuation f i 4 influence factors, and establishing a prediction early warning model based on landslide strain evolution. Taking the 4 influence factors as input variables, and decomposing the sliding surface time sequence strain into near-steady-state, acceleration deformation and steady-state 3 characteristic deformation clusters Clusterj according to the evolution characteristic clusters thereof as output variables; and finding out a prediction rule corresponding to the characteristic deformation cluster of which the target output variable is the acceleration deformation, namely a hydro-meteorological early warning threshold value of the acceleration deformation of the landslide. According to the high-spatial-resolution strain sensing optical cable, the sliding surface is accurately positioned, the sliding surface strain change is captured, a more reliable hydrological early warning threshold can be obtained based on the prediction early warning model of the sliding surface strain evolution, and the problems of insufficient durability of accurate monitoring of large deformation of the deep part of the sliding slope in a reservoir area and high early warning false alarm rate based on the surface deformation are solved.
The beneficial effects are that: compared with the prior art, the invention has the following advantages:
(1) The sub-meter-scale high-spatial-resolution strain sensing optical cable can accurately position a potential sliding surface and capture the deformation of the sliding surface; the prediction early warning model based on the sliding surface strain evolution can obtain a more reliable hydrological early warning threshold value, and solves the problems of insufficient durability of accurate monitoring of deep large deformation of a landslide in a reservoir area and high early warning false alarm rate based on earth surface deformation.
(2) The ultra-weak reflection grating strain sensing optical cable can remotely monitor the strain distribution and evolution process of the underground full drilling depth of the landslide in real time, is particularly suitable for identifying a plurality of thin-layer sliding surfaces, and is characterized in that the sliding body acceleration motion under the shearing action is represented as a peak strain characteristic at the sliding surface.
(3) Compared with FBG, the strain data obtained based on the ultra-weak reflection grating has equivalent precision, reaches 1-2 mu epsilon, has spatial resolution better than 0.5m and higher than that of the FBG, and realizes accurate monitoring of long-distance and submicron-scale high spatial resolution; compared with DFOS, the invention has high automation degree and lower system cost, and realizes remote real-time monitoring.
(4) The deformation monitoring method based on the sliding surface strain is suitable for long-term monitoring of the sliding slope deformation of a reservoir area, reflects the real motion state of the sliding slope, and has high accuracy and strong practicability compared with the early-warning prediction based on the surface deformation, and the early-risk early-warning method based on the underground deformation (strain) evolution greatly reduces the false alarm rate and avoids frequent excessive early warning.
(5) The early warning method for the landslide in the reservoir area provided by the invention simultaneously considers a plurality of influencing factors (rainfall, rainfall intensity, reservoir water level elevation and water level fluctuation), and is more reasonable than the traditional early risk early warning threshold criterion for the landslide in the reservoir area.
Drawings
FIG. 1 is a schematic diagram of a reservoir landslide monitoring system employed in the present invention;
FIG. 2 is a schematic illustration of a real-time in situ automated monitoring of borehole installation strain in accordance with the present invention;
FIG. 3 is a flow chart of an underground strain monitoring and early warning method according to an embodiment of the present invention;
FIG. 4 is a graph of monitoring results of surface displacement and subsurface strain of a landslide of a reservoir under the synergistic effect of rainfall and reservoir water level in an embodiment of the invention;
FIG. 5 is a graph of cumulative strain clustering results for deep slip surface SS2 according to an embodiment of the present invention;
fig. 6 is a landslide hazard hydrographic warning indicator of an embodiment of the invention.
Detailed Description
As shown in fig. 1-6, the reservoir landslide deformation monitoring and early warning method based on the sliding surface strain evolution is realized by adopting a reservoir landslide monitoring system, the deformation monitoring is underground strain monitoring based on vertical drilling, and potential sliding surfaces are identified through the strain distribution of full drilling, namely, the sliding surfaces are characterized in that the strain is rapidly increased when the sliding surface accelerates. The early warning is a hydrographic warning aiming at the remarkable acceleration deformation of the landslide body during monitoring. The reservoir area landslide deformation monitoring and early warning method based on the sliding surface strain evolution comprises the following steps:
(1) Monitoring station site selection and drilling: an active landslide partition 1 and a flat site vertical drilling 3 are selected, a monitoring station is established, and a river is shown as 2 in fig. 1. The vertical borehole diameter is 110mm and is drilled at least 3m below the complete formation. The monitoring station comprises an in-situ monitoring unit, a data acquisition unit, a data transmission unit 6 and a user access unit 8. Wherein the in-situ monitoring unit comprises one or more vertical holes 3 provided with strain sensing optical cables; the data acquisition unit is 1 ultra-weak reflection grating demodulator 5. The data transmission unit 6 is a wireless transmission module and is integrated in the earth surface monitoring station protection box 7. The user access unit 8 is a computer.
(2) Installing a strain sensing optical cable: and (3) after the drilling 3 in the step (1) is completed, the ultra-weak reflection grating strain sensing optical cable 4 is installed in an inverted-type layout mode. The ultra-weak reflection grating strain sensing optical cable 4 is an ultra-weak reflection grating strain sensing optical cable with high resolution and 1.m sampling interval, one end of the ultra-weak reflection grating strain sensing optical cable is connected with a data acquisition port of an ultra-weak reflection grating demodulator 5 near an orifice, the middle position of the optical cable 4 is connected with a gravity guide hammer 43 which is fixed at the bottom of the orifice to play a role of pre-stretching optical fibers, the redundancy of the optical cable at the bottom of the orifice is 0.5m, the ultra-weak reflection grating strain sensing optical cable 4 is kept in a pre-stretching state all the time, the other end of the optical cable is led out of the orifice, and the interval of A, B sections of gratings of the ultra-weak reflection grating strain sensing optical cable 4 in vertical projection is 0.5m, namely, in the embodiment, the optical cable interval of the inverted vertical section optical cable is 0.5m. The sampling interval (namely grating pitch) d of the ultra-weak reflection grating strain sensing optical cable 4 is less than or equal to 1.0m.
In the step (2), the A, B end is redundant by at least 2m near the orifice to assist the accurate positioning of the underground depth, and is particularly beneficial to the identification of thin-layer sliding surfaces with the thickness of tens of centimeters; the end B is also connected with an acquisition jumper wire for standby.
The ultra-weak reflection grating strain sensing optical cable structure comprises a bare fiber, a coating layer, a metal-based rope-shaped reinforcing rib and an outer sheath. The gravity guide hammer 43 in the embodiment is made of metal and has a diameter of 50-60mm, a length of 600-1000mm and a weight of 20-35kg. In the ultra-weak reflection grating strain sensing optical cable 4, 41 is an ultra-weak reflection grating, and 42 is an optical fiber transmission section.
(3) And (3) backfilling drilling: after the gravity guide hammer 43 connected with the middle part of the ultra-weak reflection grating strain sensing optical cable 4 in the step (2) is lowered to the hole bottom position, the two ends of the ultra-weak reflection grating strain sensing optical cable 4 extending out of the fixed hole opening are always in a tensioning state, the hole 3 is immediately refilled, and the ultra-weak reflection grating strain sensing optical cable is sectionally filled according to the type of a rock-soil body in principle according to an actual stratum so as to be close to the distribution of the actual stratum. The complete rock interval 31, which is from the bottom of the hole to 3m deep, is filled with cement accelerator as a relatively hard formation. The upper fractured rock interval 32 is filled with fine sand; the uppermost crushed stone soil section 33 is filled with "fine sand + silty clay" in a 1:1 ratio.
(4) Remote real-time monitoring: and (3) backfilling to the hole and compacting, introducing one end of the ultra-weak reflection grating strain sensing optical cable 4 extending out of the hole into a protection box 7 on the ground surface near the hole, and connecting an optical fiber jumper wire to connect the acquisition port of the ultra-weak reflection grating demodulator 5, wherein the ultra-weak reflection grating demodulator 5, the wireless transmission module 6 and the data user access unit 8. And after software and hardware debugging of the backfill and monitoring system is finished, the drill hole 3 is placed for one month, and the backfill is solidified and stabilized to be used as a monitoring initial period. Long-term remote real-time monitoring activities are thereafter initiated. In this embodiment, the monitoring data is sampled at a maximum of 5 s/time, at a sampling frequency of 5s in a rainy season or a flood season (e.g., 5-9 months each year) in which the deformation is significant, and at a sampling frequency of 60-600s in a non-flood season.
(5) Slip plane identification and time sequence strain cluster analysis: drawing a full-borehole strain profile evolution diagram according to the underground accumulated time sequence strain dataset which is obtained by the user access unit in the step (4) and is recorded remotely in real time, and identifying a strain peak area as a potential slip surface; and extracting the sliding surface time sequence strain data, taking an average value of a plurality of strain data every day as a strain daily value, and further generating a daily value data set of the sliding surface strain rate. And qualitatively clustering the strain rate into near-steady state, accelerated deformation and steady state 3 types according to the evolution characteristics of the strain rate-time curve, namely small fluctuation of the strain rate, abrupt change of the strain rate and unchanged strain rate, wherein the numerical variable is converted into the type variable. It is noted that during clustering, high strain rates (> 300. Mu.. Epsilon./d) of 2 consecutive days or more are considered to accelerate deformation, and single high strain rate islands are considered to be near steady state. The number of cumulative strain clusters is either manually specified or automatically determined by common clustering algorithms.
(6) Establishing a prediction early warning model and outputting a threshold criterion: and (3) taking the clustering result Clusterj of the slip surface strain rate obtained in the step (5) as an output variable, and taking the rainfall daily value data set and the library water level elevation data set as input variables to establish a prediction early warning model.
For this example, the model training results are shown in FIG. 6. First, rainfall R i Intensity of rainfall I i Height L of reservoir water level i And water level fluctuation f i The daily value dataset of (1) predicts the input variable of the early warning model, and the strain rate clustering result Clusterj is set as the output variable; secondly, dividing all samples into a training set and a testing set according to a ratio of 7:3, and setting the self-adaptive enhancement iteration number to be 10; then, the predictive early warning model starts to calculate, and the grouping variable and the segmentation threshold are determined, and each branch result is shown in fig. 6. The depth of the decision tree is 8, and the height L of the water level in the layer 1 of the tree is based on the standard of the maximum information gain rate i Selected as the optimal grouping variable, the library water level elevation is divided into two groups according to the minimum group limit value 170.0m obtained by MDLP binning, and the library water level elevation is divided into two groups at RWL>170.0m, it is judged that it is no longer meaningful to continue grouping according to the pruning algorithm, so this branch is no longer growing. And similarly, growing and pruning the k-layer decision tree according to the standard.
The growth process of the decision tree in the model is a process of continuously grouping input variables according to output variables. The branches of the decision tree are in response to rainfall R i Intensity of rainfall I i Height L of reservoir water level i And water level fluctuation f i The data sets are gradually grown in the continuous grouping process, and when the continuous grouping of the data sets is not meaningful, the growing process of the decision tree is finished.
And deducing the value of an output variable, namely a strain rate clustering result, such as Cluster2, according to the value of the new data input variable by the predictive early warning model. In sum, according to the generated prediction early warning model, a threshold criterion of landslide underground strain evolution and external driving factors (rainfall and reservoir water level) is established, and early risk early warning based on the external driving factors is realized.
As shown in figures 3-6, large-scale landslide widely distributed in three gorges reservoir areas in China is taken as a research object, and the deformation is mainly controlled by annual circulating reservoir water level scheduling (145-175 m fluctuation) and seasonal rainfall (annual average precipitation exceeds 1000 mm). During in situ monitoring, it was found that both the earth displacement (D1-D5) obtained by the GNSS installed near the borehole and the subsurface strain of the present invention (here SS1 and SS2 are the time-series strain of the shallow secondary slip and deep primary slip, respectively) showed acceleration movements at 7 months and 6 days (daily rainfall 153.3 mm). According to the existing weather early warning index, the rainstorm during the monitoring period (daily rainfall reaches 50 mm) is four times, but landslide movement is not caused by each rainstorm, and particularly, the heavy rainstorm with the rainfall of 97.7mm in 26 days of 8 months is the best demonstration (figure 4). The strain Cluster analysis of the most representative deep slip surface SS2 is shown in fig. 5, and the acceleration segment clusters as Cluster2. And taking the normalized rainfall daily value data set and the library water level elevation data set as input variables and the accumulated strain clustering result as output variables, and establishing a prediction early warning model. The sample data are divided into a training sample and a test sample according to the ratio of 7:3, the overall accuracy of the training sample reaches 92.3%, and the overall accuracy of the test sample reaches 90.6%, which indicates that the accuracy of the prediction model is high. By adopting the early warning method based on underground strain evolution, the early warning threshold value is obtained that the height of the reservoir water level RWL is 146.5-149.5 m and the daily rainfall is more than 58mm, or the reservoir water level RWL is less than 170m and the daily rainfall intensity is more than 26.6mm/h.
Claims (10)
1. A reservoir area landslide deformation monitoring and early warning method based on sliding surface strain evolution is characterized in that: the method comprises the following steps:
(1) Drilling holes (3) below the rock stratum in landslide subareas and establishing monitoring stations, wherein the monitoring stations comprise an in-situ monitoring unit, an ultra-weak reflection grating demodulator (5), a data transmission unit (6) and a user access unit (8);
(2) An ultra-weak reflection grating strain sensing optical cable (4) is arranged in the drilling hole (3) in an inverted mode, a gravity guide hammer (43) connected in the ultra-weak reflection grating strain sensing optical cable (4) is arranged at the bottom of the drilling hole, and one end of the ultra-weak reflection grating strain sensing optical cable (4) is connected with an ultra-weak reflection grating demodulator (5);
(3) And (3) backfilling drilling: after the gravity guide hammer (43) reaches the bottom of the hole, designating one end of the ultra-weak reflection grating strain sensing optical cable (4) extending out of the hole (3) as a collecting end and the other end as a standby collecting end; backfill holes (3) are filled in sections according to the type of the rock-soil body of the stratum;
(4) Remote real-time monitoring: backfilling the step (3) to an orifice, compacting, introducing one end of an ultra-weak reflection grating strain sensing optical cable (4) into a protection box (7) and connecting the ultra-weak reflection grating demodulator (5), debugging the ultra-weak reflection grating demodulator (5), a data transmission unit (6) and a user access unit (8), and establishing time sequence strain data sets of rock and soil bodies with different depths in the underground;
(5) Slip plane identification and time sequence strain cluster analysis: extracting a time sequence strain data set which is obtained by a user access unit (8) and is recorded remotely in real time in the step (4), and identifying a strain peak area as a potential slip surface according to strain distribution along a full drill hole; taking an average value of a plurality of strain data of a potential slip surface every day as a strain daily value, and differentiating to obtain a daily value data set of the strain rate; qualitatively clustering the slip plane strain rate into three clustering results of near steady state, acceleration deformation and steady state;
(6) Establishing a predictive early warning model and a threshold criterion: taking the clustering result of the slip surface strain rate obtained in the step (5) as an output variable, and taking rainfall R as an output variable i Intensity of rainfall I i Height L of reservoir water level i And water level fluctuation f i The daily value data set of (1) is used as an input variable to establish a prediction early warning model, and the prediction early warning model deduces output changes according to the value of the input variableAnd (3) setting up a value of the quantity, establishing a threshold criterion of landslide underground strain evolution and external driving factors such as rainfall, rainfall intensity, reservoir water level elevation and reservoir water level fluctuation, and early warning the landslide deformation of a reservoir area.
2. The method for monitoring and early warning of landslide deformation of a reservoir area based on sliding surface strain evolution according to claim 1, which is characterized by comprising the following steps: in the step (6), sample data in the prediction early-warning model is divided into a training set and a testing set, the iteration times are set, the prediction early-warning model is repeatedly trained through the input of the training set, the testing set is used for classifying and predicting the generated prediction early-warning model when the precision of the generated prediction early-warning model reaches a set range, and the value of an output variable is deduced according to the value of an input variable by the generated prediction early-warning model.
3. The method for monitoring and early warning of landslide deformation of a reservoir area based on sliding surface strain evolution according to claim 1, which is characterized by comprising the following steps: in the step (3), a stratum section (31) with the depth of 3m from the bottom of the hole is filled with cement accelerator to serve as a hard stratum; the fractured rock interval (32) is filled with fine sand; the gravel soil segments (33) are filled with fine sand and silty clay.
4. The method for monitoring and early warning of landslide deformation of a reservoir area based on sliding surface strain evolution according to claim 1, which is characterized by comprising the following steps: in the step (5), the sliding surface strain rates are qualitatively clustered according to the evolution characteristics of the sliding surface strain rates over time, wherein the evolution characteristics of the sliding surface strain rates over time comprise small fluctuation of the strain rates, abrupt change of the strain rates and unchanged strain rates.
5. The method for monitoring and early warning of landslide deformation of a reservoir area based on sliding surface strain evolution according to claim 1, which is characterized by comprising the following steps: the in situ monitoring unit comprises at least one aperture (3) mounted with an ultra-weak reflection grating strain sensing optical cable.
6. The method for monitoring and early warning of landslide deformation of a reservoir area based on sliding surface strain evolution according to claim 1, which is characterized by comprising the following steps: in the step (5), during time-series strain cluster analysis, a data backtracking principle is followed to eliminate noise errors of a strain rate time sequence.
7. The method for monitoring and early warning of landslide deformation of a reservoir area based on sliding surface strain evolution according to claim 1, which is characterized by comprising the following steps: in the step (2), the sampling interval d of the ultra-weak reflection grating strain sensing optical cable (4) is less than or equal to 1.0m.
8. The method for monitoring and early warning of landslide deformation of a reservoir area based on sliding surface strain evolution according to claim 1, which is characterized by comprising the following steps: the ultra-weak reflection grating strain sensing optical cable (4) in the step (2) comprises an ultra-weak reflection grating (41) and an optical fiber transmission section (42).
9. The method for monitoring and early warning of landslide deformation of a reservoir area based on sliding surface strain evolution according to claim 1, which is characterized by comprising the following steps: in the step (2), the middle position of the gravity guide hammer (43) connected in the ultra-weak reflection grating strain sensing optical cable (4) is arranged at the bottom of the hole.
10. The method for monitoring and early warning of landslide deformation of a reservoir area based on sliding surface strain evolution according to claim 1, which is characterized by comprising the following steps: in the step (2), the ultra-weak reflection grating strain sensing optical cable (4) is installed in an inverted mode in the drilling hole (3) and pre-tensioned until the spatial resolution is better than 0.5m.
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